About Master of Data Science and Innovation
Welcome to the central page about UTS Master of Data Science and Innovation (MDSI). This page has been designed as a central area for all the aspects of MDSI, and the application process.
As you read through the information below, you will find many links to other areas of the UTS website which contain useful information. Please note you can always come back to this page to reorient yourself, and continue on your journey with this course.
Ready to Apply? Visit the 'how to apply' page for important information and links to submit your application.
Ready for more details about the MDSI? The best place to hear from and speak to the teaching team, current MDSI students and to ask all your questions is at one of our information sessions for prospective students.
- Watch a recording of our recent online webinar here
- Find out what info sessions and events are coming up here
- Download our postgraduate course guide
About Master of Data Science and Innovation
The UTS Master of Data Science and Innovation is a ground-breaking program of study. It’s the first transdisciplinary data science degree offered in Australia where creativity and innovation are integral components. The MDSI equips students with the skills and expertise they’ll need for a rewarding career in data science and analytics.
Taking a transdisciplinary approach, the course utilises a range of perspectives from diverse fields and integrates them with industry experiences, real-world projects and self-directed study, equipping graduates with an understanding of the potential of analytics to transform practice. The course is delivered in a range of modes, including face-to-face learning and contemporary online experiences in UTS's leading-edge facilities.
Speaker: What’s unique about the course, first and foremost, is that students engage with data science and innovation. So looking at those two things together is something that makes us a pretty special program. We were the first in Australia to tackle both those aspects and put them together.
New speaker: The teaching style of this course is a little bit different from a conventional university course. We do have lectures, obviously. But there’s a lot of project work, there’s a lot of group work. And there’s a lot of practical work through our interactions and our connections with industry.
New speaker: The way we are learning is very open and dynamic. You get the theory, but you have to put in practice almost straightaway. So it’s really focused on how you apply your learning and make sure you learn it right.
New speaker: What I’ve really enjoyed about the MDSI is that they’ve expanded on what we can do to get assessed for our knowledge. One example of that is that they’ve really encouraged us to participate in hackathons, which is a great chance for students to demonstrate real-world skills in the data science space outside of a traditional university framework, and learn a whole lot in the process.
New speaker: The kinds of hackathons we focus on in MDSI are data hackathons. So that’s where organisations come in with some burning questions that they have. That they may have been trying to solve themselves, and they put it out as an open challenge.
New speaker: We have a deep engagement with industry. Our connections to partners from industry help us to shape our curriculum. We also look to those connections to help us to deliver the curriculum. In addition to that, we’ve been crafting a number of opportunities as internships. So that our students have an opportunity to actually sit within an organisation. And experience data science practice in a real-life, day-to-day context.
New speaker: Data science is a capability that’s growing to become a part of every industry. Everybody can take advantage of data science. I’ve spent the last six months mentoring one of the master’s students in their first project unit. Together we’ve been exploring how to analyse social media data in real-time. To make better customer decisions about how we can support and provide great customer service to those in a much quicker way.
New speaker: Already I utilise what I’m learning in my day-to-day job. So I sit in the data science team where I’m employed and I can really see the application and the output. So that’s really beneficial, but I also think that it’s all of the skills that I’m learning enable me to shape my career with exciting opportunities.
New speaker: Data science and big data are so important. Because it allows us to take real-world data and turn it into something that can inform decision making. And informing decision making that will actually change and help people’s lives. Improve people’s lives.
New speaker: I think an important aspect of data science that people don’t realise is that there is a creative element to it. Data doesn’t speak for itself. You have to bring your own perspective to it, and you can shape the story that you tell. And that story, how it’s shaped, doesn’t reside in the data itself. It resides in how you think about the data. Your background, your ethics, your values. And these are kinds of things that are becoming ever more important in the present-day world.
Why study MDSI?
This course is offered on a two-year, full-time or four-year, part-time basis (dependent on the number of subjects undertaken each session), with intakes in both Autumn and Spring session each year.
When are classes held?
The Master of Data Science and Innovation is designed with flexibility in mind. Classes are held after 6pm on weekdays and all day Saturdays. They’re also not held every week, so a typical subject may have three evenings (6-9pm) and two Saturdays (9am - 5pm) sessions over the session.
A full- time load is three subjects, so it is possible to schedule class around full-time work. What students usually find the most challenging is finding time for assessments and other pre-class work, which is often why students elect to study part-time.
Applicants must have completed a UTS recognised bachelor's degree, or an equivalent or higher qualification, or submitted other evidence of general and professional qualifications that demonstrates potential to pursue graduate studies.
All applicants must satisfy the following requirement:
- bachelor degree, or higher qualification, in a relevant discipline, such as:
- information technology
- mathematical sciences
- physics and astronomy
- business and management
- finance and related fields
- or economics and econometrics.
If the applicant's academic qualification is not listed in the above disciplines but they do have at least two years full time work experience in data analytics, database management or programming related fields, then they must also provide:
- a C.V. outlining work experience and education, as well as other relevant evidence and information and
- an official Statement of Service, from the employer, confirming the dates of employment, and a description of the position held within the organisation.
The English proficiency requirement for international students or local applicants with international qualifications is: Academic IELTS: 6.5 overall with a writing score of 6.0; or TOEFL: paper based: 550-583 overall with TWE of 4.5, internet based: 79-93 overall with a writing score of 21; or AE5: Pass; or PTE: 58-64; or CAE: 176-184.
Eligibility for admission does not guarantee offer of a place.
If you don't meet the admission requirements for this course, there may be alternative pathways to help you gain admission. Contact us at email@example.com to discuss pathway options.
For domestic students, course fee information can be found by using the UTS Course Fee Calculator tool, and following the steps below:
- Choose 'Search for fees by course'.
- fee type 'Postgraduate Domestic Coursework'
- fee year 2021
- cohort 2021
- course area ‘Transdisciplinary Innovation’
- course code ‘C04372’
To calculate the total cost of the course, multiply the 'Total CP' by the ‘Fee per CP'.
Note: Commonwealth supported places are not available for MDSI.
If you do have to pay a fee and you're a local student, you may be eligible for FEE-HELP, an Australian Government loan scheme. Using FEE-HELP means you don't have to pay for your tuition fees up front. More information about FEE-HELP can be found at uts.edu.au/government-help-schemes
You can choose to repay your FEE-HELP loan simply by notifying your employer who will then withhold your payments through the PAYG tax system. You can also make payments directly to the Australian Taxation Office (ATO).
If you've already completed a degree at UTS then you're eligible for the Alumni Advantage program, which offers a 10% savings on full fee paying degree programs. Find out if you're eligible for the Alumni Advantage at: alumni.uts.edu.au/advantage
If you’re an international student, head to uts.edu.au/international to find the course information, fees and application details relevant to you.
Students must complete 96 credit points (CP), comprising 44CP core and 52CP optional subjects. Optional subjects can be selected from specified data science related optional subjects and from across the University’s disciplines. Enrolment in subjects from other disciplines is dependent on approval from the Course Director and subject coordinator, and usually requires demonstrated ability to meet pre-requisites. This flexible course structure enables students to pursue their own particular interests and career aspirations.
For more detailed information on the course structure, and information on each of the below subjects visit our UTS Handbook.
96CP = 44CP Core Subjects + 52CP Optional Subjects
Core Subjects - Select 44CP:
- Data Science for Innovation 8cp
- Statistical Thinking for Data Science 8cp
- Data, Algorithms and Meaning 8cp
- Data Visualisation and Narratives 8cp
- iLab 1 12cp
- iLab 2 12cp
Optional Subjects (choose 52CP from the following*):
- Leading Data Science Initiatives 8cp
- Data and Decision Making 8cp
- Deep Learning 8cp
- Data Science Practice 8cp
- Elective 1 6cp
- Elective 2 6cp
- Elective 3 6cp
- Elective 4 6cp
Total Credit Points 96cp
* Please note the optional subject list is reviewed every year and is subject to change according to student demand.
Recognition of Prior Learning
A maximum of 32 credit points of exemptions may be considered for electives. Exemptions are granted only on the basis of prior postgraduate study at an Australian university, or at a recognised overseas institution deemed to be equivalent to an Australian university.
To be eligible for recognition of prior learning, the subject being considered for prior study must have been completed within five years of commencing the course. Recognition of study completed before this period is not considered.
Come along to an information session
Ready for more details about the MDSI? The best place to hear from the teaching team, current MDSI students and to ask all your questions is at one of our information sessions for prospective students.